In [2]:
from IPython.display import Image
Image(filename="/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/gp_manhattan.png")
Out[2]:
Date: 2021_03_18
Analyzed by: Sara Saez Atienzar
Priority index is designed to discover and prioritize drug targets at the gene and pathway level. Taking as inputs GWAS summary statistics for immune-related traits/diseases, Pi uses genomic predictors to identify and score genomic seed genes likely responsible for these genetic associations, including nearby genes nGene based on genomic proximity and genomic organisation; chromatin conformation genes cGene based on summary data produced from promoter capture Hi-C studies; and expression-associated genes eGene based on summary data produced from eQTL mapping.
Information about Priority Index can be found at: http://pi314.r-forge.r-project.org Citation Hai Fang, The ULTRA-DD Consortium, Julian C Knight. Pi: an R/Bioconductor package leveraging genetic evidence to prioritise drug targets at the gene and pathway level. Bioconductor (2020); doi:10.18129/B9.bioc.Pi
Get the GWAS input ready for the analysis It is essential to format the GWAS summary Stats. in a format that is readable by the software. This should include:
%%bash
echo "Directory where the analysis where performed"
pwd
ls
cd PriorityIndex
echo "Where I moved the results and files"
pwd
ls
Directory where the analysis where performed /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG 2021_03_18.PriorityIndex.MG.ipynb 2021_03_19.MendelianRandomization.MG.ipynb MR_Base PriorityIndex Where I moved the results and files /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex 2021_03_18.PriorityIndex_MG.tab MG_CLEAN.allheader.291585variants.tab MG.rsID.CLEAN.tab.gz Rplots.pdf
%%bash
module load R/4.0.3
R --vanilla --no-save
#Working Directory for the non-duplicated data
require(dplyr)
require(tidyverse)
require(data.table)
library(Pi)
sessionInfo()
set.seed(1234)
#GWAS summary stats
data= fread("/data/NDRS_LNG/MyastheniaGravis/updated.April2020/MetaAnalysis.hg38.ByCohort.rerun/USmerged.Itals.hg38/META_MG.USmerged.Itals.UNRELATED.hg38.Rsq03.glm_filteredDirection.HetISq80MAF001cases.rsid.txt", header = T)
head(data)
# I have applied some filters steps:
data2 = filter(data, maf_EA.CASE >0.05 | maf_EA.CTRL >0.05)
data3 = filter(data2, OtherAllele == "A" | OtherAllele == "G" | OtherAllele == "T" | OtherAllele == "C")
data4 = filter(data3, EffectAllele == "A" | EffectAllele == "G" | EffectAllele == "T" | EffectAllele == "C")
data5 = filter(data4, P <0.05)
#Formatting for PI analysis
data6 = data5 %>% filter(P <5e-8) %>% mutate(suggestive=FALSE)
data6 = data5%>% mutate(suggestive=TRUE) %>% as.data.frame()
data7 = data6%>% filter(P <5e-8) %>% mutate(suggestive=FALSE)
data8 = rbind(data6, data7)
table(data8$suggestive)
#FALSE TRUE
# 168 334357
write.table(data8, "MG_CLEAN.allheader.291585variants.tab", quote = F, sep = "\t", row.names = F)
data8$snp = data8$rsID
data8$effect = data8$EffectAllele
data8$other = data8$OtherAllele
data8$p = data8$P
data8$b = data8$beta
data8$se = data8$StdErr
data9 = select(data8, snp,p,effect, other,b,se,suggestive)
write.table(data9, "MG.rsID.CLEAN.tab", quote = F, sep = "\t", row.names = F)" > MR_2.R
R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
> #Working Directory for the non-duplicated data
> require(dplyr)
> require(tidyverse)
> require(data.table)
> library(Pi)
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /usr/local/intel/compilers_and_libraries_2019.1.144/linux/mkl/lib/intel64_lin/libmkl_rt.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Pi_2.2.1 dnet_1.1.7 supraHex_1.28.1 hexbin_1.28.2
[5] igraph_1.2.6 data.table_1.14.0 forcats_0.5.1 stringr_1.4.0
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.0
[13] ggplot2_3.3.3 tidyverse_1.3.0 dplyr_1.0.5
loaded via a namespace (and not attached):
[1] utf8_1.2.1 tidyselect_1.1.0
[3] RSQLite_2.2.4 AnnotationDbi_1.52.0
[5] htmlwidgets_1.5.3 grid_4.0.3
[7] BiocParallel_1.24.1 pROC_1.17.0.1
[9] munsell_0.5.0 codetools_0.2-18
[11] misc3d_0.9-0 withr_2.4.1
[13] colorspace_2.0-0 Biobase_2.50.0
[15] OrganismDbi_1.32.0 knitr_1.31
[17] rstudioapi_0.13 stats4_4.0.3
[19] ROCR_1.0-11 MatrixGenerics_1.2.1
[21] GenomeInfoDbData_1.2.4 bit64_4.0.5
[23] vctrs_0.3.6 generics_0.1.0
[25] ipred_0.9-11 xfun_0.22
[27] biovizBase_1.38.0 BiocFileCache_1.14.0
[29] randomForest_4.6-14 R6_2.5.0
[31] GenomeInfoDb_1.26.2 AnnotationFilter_1.14.0
[33] bitops_1.0-6 cachem_1.0.4
[35] reshape_0.8.8 DelayedArray_0.16.2
[37] assertthat_0.2.1 scales_1.1.1
[39] nnet_7.3-15 debugme_1.1.0
[41] gtable_0.3.0 ggbio_1.38.0
[43] ensembldb_2.14.0 timeDate_3043.102
[45] rlang_0.4.10 splines_4.0.3
[47] rtracklayer_1.50.0 lazyeval_0.2.2
[49] ModelMetrics_1.2.2.2 dichromat_2.0-0
[51] broom_0.7.5 checkmate_2.0.0
[53] BiocManager_1.30.10 reshape2_1.4.4
[55] modelr_0.1.8 GenomicFeatures_1.42.2
[57] ggnetwork_0.5.8 backports_1.2.1
[59] Hmisc_4.5-0 RBGL_1.66.0
[61] caret_6.0-86 tcltk_4.0.3
[63] tools_4.0.3 lava_1.6.9
[65] ellipsis_0.3.1 RColorBrewer_1.1-2
[67] BiocGenerics_0.36.0 Rcpp_1.0.6
[69] plyr_1.8.6 base64enc_0.1-3
[71] progress_1.2.2 zlibbioc_1.36.0
[73] RCurl_1.98-1.2 ps_1.6.0
[75] prettyunits_1.1.1 rpart_4.1-15
[77] openssl_1.4.3 S4Vectors_0.28.1
[79] SummarizedExperiment_1.20.0 haven_2.3.1
[81] ggrepel_0.9.1 cluster_2.1.1
[83] fs_1.5.0 crul_1.1.0
[85] magrittr_2.0.1 reprex_1.0.0
[87] ProtGenerics_1.22.0 matrixStats_0.58.0
[89] hms_1.0.0 XML_3.99-0.5
[91] jpeg_0.1-8.1 readxl_1.3.1
[93] IRanges_2.24.1 gridExtra_2.3
[95] shape_1.4.5 compiler_4.0.3
[97] biomaRt_2.46.3 crayon_1.4.1
[99] htmltools_0.5.1.1 Formula_1.2-4
[101] lubridate_1.7.10 DBI_1.1.1
[103] dbplyr_2.1.0 MASS_7.3-53.1
[105] rappdirs_0.3.3 Matrix_1.3-2
[107] cli_2.3.1 parallel_4.0.3
[109] Gviz_1.34.0 gower_0.2.2
[111] GenomicRanges_1.42.0 pkgconfig_2.0.3
[113] GenomicAlignments_1.26.0 RCircos_1.2.1
[115] osfr_0.2.8 foreign_0.8-81
[117] recipes_0.1.15 xml2_1.3.2
[119] foreach_1.5.1 XVector_0.30.0
[121] prodlim_2019.11.13 rvest_1.0.0
[123] VariantAnnotation_1.36.0 digest_0.6.27
[125] graph_1.68.0 httpcode_0.3.0
[127] Biostrings_2.58.0 cellranger_1.1.0
[129] htmlTable_2.1.0 curl_4.3
[131] Rsamtools_2.6.0 lifecycle_1.0.0
[133] nlme_3.1-152 jsonlite_1.7.2
[135] askpass_1.1 BSgenome_1.58.0
[137] fansi_0.4.2 pillar_1.5.1
[139] lattice_0.20-41 GGally_2.1.1
[141] fastmap_1.1.0 httr_1.4.2
[143] survival_3.2-7 glue_1.4.2
[145] png_0.1-7 iterators_1.0.13
[147] plot3D_1.3 glmnet_4.1-1
[149] bit_4.0.4 Rgraphviz_2.34.0
[151] class_7.3-18 stringi_1.5.3
[153] blob_1.2.1 latticeExtra_0.6-29
[155] memoise_2.0.0 ape_5.4-1
> set.seed(1234)
>
> data= fread("/data/NDRS_LNG/MyastheniaGravis/updated.April2020/MetaAnalysis.hg38.ByCohort.rerun/USmerged.Itals.hg38/META_MG.USmerged.Itals.UNRELATED.hg38.Rsq03.glm_filteredDirection.HetISq80MAF001cases.rsid.txt", header = T)
> head(data)
MarkerName CHROM POS OtherAllele EffectAllele maf_EA.CASE maf_EA.CTRL
1: 1:722408:C:G 1 722408 C G 0.16310731 0.15488040
2: 1:722700:G:A 1 722700 G A 0.01308062 0.01739071
3: 1:727233:G:A 1 727233 G A 0.01094501 0.01520484
4: 1:727242:G:A 1 727242 G A 0.08168713 0.09434974
5: 1:727717:C:G 1 727717 C G 0.14468767 0.13229310
6: 1:758351:A:G 1 758351 A G 0.08061933 0.09459720
beta StdErr P Direction HetISq rsID
1: 0.0594 0.0495 0.22990 -+ 68.7 rs75935175
2: -0.2150 0.1581 0.17400 -- 0.0 rs1415481957
3: -0.0362 0.1687 0.82990 +- 0.0 rs151190501
4: 0.1044 0.0661 0.11400 ++ 0.0 rs61769339
5: 0.1063 0.0522 0.04161 -- 50.8 rs61769340
6: 0.1025 0.0663 0.12210 -- 0.0 rs12238997
>
> data2 = filter(data, maf_EA.CASE >0.05 | maf_EA.CTRL >0.05)
> data3 = filter(data2, OtherAllele == "A" | OtherAllele == "G" | OtherAllele == "T" | OtherAllele == "C")
> data4 = filter(data3, EffectAllele == "A" | EffectAllele == "G" | EffectAllele == "T" | EffectAllele == "C")
> data5 = filter(data4, P <0.05)
> data6 = data5 %>% filter(P <5e-8) %>% mutate(suggestive=FALSE)
> data6 = data5%>% mutate(suggestive=TRUE) %>% as.data.frame()
> data7 = data6%>% filter(P <5e-8) %>% mutate(suggestive=FALSE)
> data8 = rbind(data6, data7)
> table(data8$suggestive)
FALSE TRUE
168 334357
> #FALSE TRUE
> # 168 334357
> write.table(data8, "MG_CLEAN.allheader.291585variants.tab", quote = F, sep = "\t", row.names = F)
>
> data8$snp = data8$rsID
> data8$effect = data8$EffectAllele
> data8$other = data8$OtherAllele
> data8$p = data8$P
> data8$b = data8$beta
> data8$se = data8$StdErr
> data9 = select(data8, snp,p,effect, other,b,se,suggestive)
> write.table(data9, "MG.rsID.CLEAN.tab", quote = F, sep = "\t", row.names = F)
>
[-] Unloading gcc 9.2.0 ...
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[+] Loading pcre2 10.21 ...
[+] Loading R 4.0.3
The following have been reloaded with a version change:
1) R/4.0 => R/4.0.3
Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.3 ✔ purrr 0.3.4
✔ tibble 3.1.0 ✔ stringr 1.4.0
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✔ readr 1.4.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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Loading required package: data.table
Attaching package: ‘data.table’
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Loading required package: dnet
Loading required package: supraHex
Loading required package: hexbin
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Warning messages:
1: replacing previous import 'ROCR::plot' by 'graphics::plot' when loading 'Pi'
2: no DISPLAY variable so Tk is not available
%%bash
head MG.rsID.CLEAN.tab | column -t
module load samtools
bgzip MG.rsID.CLEAN.tab
snp p effect other b se suggestive rs61769340 0.04161 G C 0.1063 0.0522 TRUE rs116030099 0.01702 C T -0.1757 0.0736 TRUE rs79010578 0.03955 A T 0.1344 0.0653 TRUE rs61768170 0.009184 C G 0.1569 0.0602 TRUE rs10157329 0.02772 A T 0.1451 0.0659 TRUE rs7515915 0.04625 G T 0.1171 0.0587 TRUE rs60320384 0.03328 G C 0.1257 0.059 TRUE rs59066358 0.04431 A G 0.1189 0.0591 TRUE rs61768207 0.03744 A G 0.1438 0.0691 TRUE
[+] Loading samtools 1.11 ...
%%bash
module load R/4.0.3
R --vanilla --no-save
require("readr")
require(dplyr)
require(tidyverse)
require(data.table)
library(Pi)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
data.file <- file.path("/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/", "MG.rsID.CLEAN.tab.gz")
data <- readr::read_delim(data.file, delim="\t") %>% as.data.frame()
#Parameters to use
#Parameters to use
include.LD <- 'EUR'
LD.r2 <- 0.8
LD.customised <- NULL
significance.threshold <- 5e-8
score.cap <- 10
distance.max <- 20000
decay.kernel <- "constant"
decay.exponent <- 2
GR.SNP <- "dbSNP_GWAS"
#GR.SNP <- xRDataLoader('dbSNP_GWAS', RData.location=RData.location) # equivalent to this
GR.Gene <- "UCSC_knownGene"
#GR.Gene <- xRDataLoader('UCSC_knownGene', RData.location=RData.location) # equivalent to this
include.TAD <- "GM12878" # lymphoblast, reflective of immune-context genomic organisation
include.HiC <- c("Monocytes","Macrophages_M0","Macrophages_M1","Macrophages_M2","Neutrophils","Naive_CD4_T_cells","Total_CD4_T_cells","Naive_CD8_T_cells","Total_CD8_T_cells","Naive_B_cells","Total_B_cells")
include.eQTL <- c("Bcell","Blood","CD14","CD4","CD8","IFN","LPS24","LPS2","Neutrophil","NK")
eQTL.customised <- NULL
cdf.function <- "empirical"
scoring.scheme <- 'max'
network <- "STRING_high"
STRING.only <- NA
weighted <- FALSE
network.customised <- NULL
#network.customised <- xDefineNet("STRING_high", RData.location=RData.location) # equivalent to this
seeds.inclusive <- TRUE
normalise <- "laplacian"
restart <- 0.7
normalise.affinity.matrix <- "none"
parallel <- TRUE
multicores <- NULL
verbose <- TRUE
#Prioritisation analysis
# prepare predictors
## first, genomic predictors
ls_pNode_genomic <- xPierSNPsAdvABF(data, include.LD=include.LD, LD.customised=LD.customised, LD.r2=LD.r2, significance.threshold=significance.threshold, score.cap=score.cap, distance.max=distance.max, decay.kernel=decay.kernel, decay.exponent=decay.exponent, GR.SNP=GR.SNP, GR.Gene=GR.Gene, include.TAD=include.TAD, include.eQTL=include.eQTL, include.HiC=include.HiC, cdf.function=cdf.function, scoring.scheme=scoring.scheme, network=network, STRING.only=STRING.only, weighted=weighted, network.customised=network.customised, seeds.inclusive=seeds.inclusive, normalise=normalise, restart=restart, normalise.affinity.matrix=normalise.affinity.matrix, parallel=parallel, multicores=multicores, verbose=verbose, RData.location=RData.location)
## then, annotation predictors
data.file <- file.path(RData.location, "iAnno.txt")
iA <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)[,c("Symbol","OMIM","Phenotype","Function")]
colnames(iA) <- c("Symbol","dGene","pGene","fGene")
ls_pNode_anno <- lapply(2:4, function(j){
data_anno <- subset(data.frame(seed=iA$Symbol,weight=iA[,j],stringsAsFactors=F), weight>0)
pNode <- xPierAnno(data_anno, list_pNode=ls_pNode_genomic, network=network, STRING.only=STRING.only, weighted=weighted, network.customised=network.customised, seeds.inclusive=seeds.inclusive, normalise=normalise, restart=restart, normalise.affinity.matrix=normalise.affinity.matrix, parallel=parallel, multicores=multicores, verbose=verbose, RData.location=RData.location)
})
names(ls_pNode_anno) <- colnames(iA)[2:4]
## bring together both predictors
ls_pNode <- c(ls_pNode_anno, ls_pNode_genomic)
# Prioritisation in a discovery mode
dTarget <- xPierMatrix(ls_pNode, displayBy="pvalue", aggregateBy="fishers", RData.location=RData.location)
#write results
write.table(dTarget$priority, "2021_03_18.PriorityIndex_MG.tab", quote = F, sep = "\t", row.names = F)
%%bash
ls /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG
mkdir PriorityIndex
mv 2021_03_18.PriorityIndex_MG.tab /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/
mv MG_CLEAN.allheader.291585variants.tab /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/
mv MG.rsID.CLEAN.tab.gz /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/
mv Rplots.pdf /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/
2021_03_18.PriorityIndex_MG.tab 2021_03_19.MendelianRandomization.MG.ipynb MG_CLEAN.allheader.291585variants.tab MG.rsID.CLEAN.tab.gz MR_Base MyastheniaGravis.ipynb Rplots.pdf
## Select the top 30 ranked genes
To prioritaze the genes with a more therapeutic potential among the top 30, I add drugability (based on PDB with pockets). This information can be obtained from http://pi.well.ox.ac.uk:3010/pidb/discovery/SJO/.
Because they are only 30 genes, I do it manually from the web.
%%bash
cd /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/
echo "name Drugability
HLA-DRB1 18
CD74 1
AP4B1 0
HLA-DRA 21
HLA-DQA1 8
UBA52 19
DCLRE1B 0
AP1M2 0
AP2A2 0
AP2S1 0
AP1M1 0
HSPA8 8
AP1S2 0
AP2M1 0
AP2A1 0
AP1S1 0
HLA-DQB1 8
AP1G1 1
SH3GL2 3
PSMD4 15
PINX1 0
ARF1 4
AP2B1 2
AP4S1 0
AP1B1 1
CLTA 0
AP4E1 0
HLA-C 1
AP1S3 1
CLTCL1 0" > Drugability.curated.top30.txt
%%bash
cd /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex
module load R/4.0.3
R --vanilla --no-save
require("readr")
require(dplyr)
require(tidyverse)
require(data.table)
library(Pi)
data = fread("2021_03_18.PriorityIndex_MG.tab", header = T)
head(data)
dim(data)
top30 = filter(data, rank <31)
head(top30)
dim(top30)
print(top30)
drugability = fread("Drugability.curated.top30.txt", header = T)
head(drugability)
dim(drugability)
merged = merge(top30,drugability)
head(merged)
dim(merged)
write.table(merged, "2021_03_18.PriorityIndex_MG.top30.drugability.tab", row.names = F, quote = F, sep = "\t")
R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
>
> require("readr")
> require(dplyr)
> require(tidyverse)
> require(data.table)
> library(Pi)
>
> data = fread("2021_03_18.PriorityIndex_MG.tab", header = T)
> head(data)
name rank rating
1: HLA-DRB1 1 5.000000
2: CD74 2 4.590645
3: AP4B1 3 4.525471
4: HLA-DRA 4 4.481652
5: HLA-DQA1 5 4.436809
6: UBA52 6 4.424590
description seed nGene cGene
1: major histocompatibility complex, class II, DR beta 1 Y 1 0
2: CD74 molecule N 0 0
3: adaptor related protein complex 4 beta 1 subunit Y 0 10
4: major histocompatibility complex, class II, DR alpha Y 0 6
5: major histocompatibility complex, class II, DQ alpha 1 Y 1 5
6: ubiquitin A-52 residue ribosomal protein fusion product 1 N 0 0
eGene dGene pGene fGene
1: 0 1 0 0
2: 0 0 0 0
3: 0 0 0 0
4: 0 1 0 1
5: 0 1 1 1
6: 0 0 0 0
> dim(data)
[1] 15163 11
> top30 = filter(data, rank <31)
> head(top30)
name rank rating
1: HLA-DRB1 1 5.000000
2: CD74 2 4.590645
3: AP4B1 3 4.525471
4: HLA-DRA 4 4.481652
5: HLA-DQA1 5 4.436809
6: UBA52 6 4.424590
description seed nGene cGene
1: major histocompatibility complex, class II, DR beta 1 Y 1 0
2: CD74 molecule N 0 0
3: adaptor related protein complex 4 beta 1 subunit Y 0 10
4: major histocompatibility complex, class II, DR alpha Y 0 6
5: major histocompatibility complex, class II, DQ alpha 1 Y 1 5
6: ubiquitin A-52 residue ribosomal protein fusion product 1 N 0 0
eGene dGene pGene fGene
1: 0 1 0 0
2: 0 0 0 0
3: 0 0 0 0
4: 0 1 0 1
5: 0 1 1 1
6: 0 0 0 0
> dim(top30)
[1] 30 11
> print(top30)
name rank rating
1: HLA-DRB1 1 5.000000
2: CD74 2 4.590645
3: AP4B1 3 4.525471
4: HLA-DRA 4 4.481652
5: HLA-DQA1 5 4.436809
6: UBA52 6 4.424590
7: DCLRE1B 7 4.330237
8: AP1M2 8 4.150376
9: AP2A2 9 4.149004
10: AP2S1 10 4.142888
11: AP1M1 11 4.123101
12: HSPA8 12 4.109843
13: AP1S2 13 4.075882
14: AP2M1 14 4.064006
15: AP2A1 15 4.063403
16: AP1S1 16 4.049329
17: HLA-DQB1 17 4.047988
18: AP1G1 18 3.996396
19: SH3GL2 19 3.960750
20: PSMD4 20 3.957381
21: PINX1 21 3.949816
22: ARF1 22 3.925540
23: AP2B1 23 3.919930
24: AP4S1 24 3.891437
25: AP1B1 25 3.867271
26: CLTA 26 3.786336
27: AP4E1 27 3.766819
28: HLA-C 28 3.764621
29: AP1S3 29 3.752658
30: CLTCL1 30 3.752224
name rank rating
description seed nGene cGene
1: major histocompatibility complex, class II, DR beta 1 Y 1 0
2: CD74 molecule N 0 0
3: adaptor related protein complex 4 beta 1 subunit Y 0 10
4: major histocompatibility complex, class II, DR alpha Y 0 6
5: major histocompatibility complex, class II, DQ alpha 1 Y 1 5
6: ubiquitin A-52 residue ribosomal protein fusion product 1 N 0 0
7: DNA cross-link repair 1B Y 0 10
8: adaptor related protein complex 1 mu 2 subunit N 0 0
9: adaptor related protein complex 2 alpha 2 subunit N 0 0
10: adaptor related protein complex 2 sigma 1 subunit N 0 0
11: adaptor related protein complex 1 mu 1 subunit N 0 0
12: heat shock protein family A (Hsp70) member 8 N 0 0
13: adaptor related protein complex 1 sigma 2 subunit N 0 0
14: adaptor related protein complex 2 mu 1 subunit N 0 0
15: adaptor related protein complex 2 alpha 1 subunit N 0 0
16: adaptor related protein complex 1 sigma 1 subunit N 0 0
17: major histocompatibility complex, class II, DQ beta 1 N 0 0
18: adaptor related protein complex 1 gamma 1 subunit N 0 0
19: SH3 domain containing GRB2 like 2, endophilin A1 N 0 0
20: proteasome 26S subunit, non-ATPase 4 N 0 0
21: PIN2/TERF1 interacting, telomerase inhibitor 1 N 0 0
22: ADP ribosylation factor 1 N 0 0
23: adaptor related protein complex 2 beta 1 subunit N 0 0
24: adaptor related protein complex 4 sigma 1 subunit N 0 0
25: adaptor related protein complex 1 beta 1 subunit N 0 0
26: clathrin light chain A N 0 0
27: adaptor related protein complex 4 epsilon 1 subunit N 0 0
28: major histocompatibility complex, class I, C Y 0 0
29: adaptor related protein complex 1 sigma 3 subunit N 0 0
30: clathrin heavy chain like 1 N 0 0
description seed nGene cGene
eGene dGene pGene fGene
1: 0 1 0 0
2: 0 0 0 0
3: 0 0 0 0
4: 0 1 0 1
5: 0 1 1 1
6: 0 0 0 0
7: 0 0 0 0
8: 0 0 0 0
9: 0 0 0 0
10: 0 0 0 0
11: 0 0 0 0
12: 0 0 0 0
13: 0 0 0 0
14: 0 0 0 0
15: 0 0 0 0
16: 0 0 0 0
17: 0 0 0 0
18: 0 0 0 0
19: 0 0 0 0
20: 0 0 0 0
21: 0 0 0 0
22: 0 0 0 0
23: 0 0 0 0
24: 0 0 0 0
25: 0 0 0 0
26: 0 0 0 0
27: 0 0 0 0
28: 2 1 0 1
29: 0 0 0 0
30: 0 0 0 0
eGene dGene pGene fGene
> drugability = fread("Drugability.curated.top30.txt", header = T)
> head(drugability)
name Drugability
1: HLA-DRB1 18
2: CD74 1
3: AP4B1 0
4: HLA-DRA 21
5: HLA-DQA1 8
6: UBA52 19
> dim(drugability)
[1] 30 2
> merged = merge(top30,drugability)
> head(merged)
name rank rating description seed
1: AP1B1 25 3.867271 adaptor related protein complex 1 beta 1 subunit N
2: AP1G1 18 3.996396 adaptor related protein complex 1 gamma 1 subunit N
3: AP1M1 11 4.123101 adaptor related protein complex 1 mu 1 subunit N
4: AP1M2 8 4.150376 adaptor related protein complex 1 mu 2 subunit N
5: AP1S1 16 4.049329 adaptor related protein complex 1 sigma 1 subunit N
6: AP1S2 13 4.075882 adaptor related protein complex 1 sigma 2 subunit N
nGene cGene eGene dGene pGene fGene Drugability
1: 0 0 0 0 0 0 1
2: 0 0 0 0 0 0 1
3: 0 0 0 0 0 0 0
4: 0 0 0 0 0 0 0
5: 0 0 0 0 0 0 0
6: 0 0 0 0 0 0 0
> dim(merged)
[1] 30 12
> write.table(merged, "2021_03_18.PriorityIndex_MG.top30.drugability.tab", row.names = F, quote = F, sep = "\t")
>
[-] Unloading gcc 9.2.0 ...
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[-] Unloading pcre2 10.21 ...
[-] Unloading R 4.0.3
[+] Loading gcc 9.2.0 ...
[+] Loading GSL 2.6 for GCC 9.2.0 ...
[-] Unloading gcc 9.2.0 ...
[+] Loading gcc 9.2.0 ...
[+] Loading openmpi 3.1.4 for GCC 9.2.0
[+] Loading ImageMagick 7.0.8 on cn1034
[+] Loading HDF5 1.10.4
[-] Unloading gcc 9.2.0 ...
[+] Loading gcc 9.2.0 ...
[+] Loading NetCDF 4.7.4_gcc9.2.0
[+] Loading pandoc 2.11.4 on cn1034
[+] Loading pcre2 10.21 ...
[+] Loading R 4.0.3
The following have been reloaded with a version change:
1) R/4.0 => R/4.0.3
Loading required package: readr
Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.3 ✔ purrr 0.3.4
✔ tibble 3.1.0 ✔ stringr 1.4.0
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── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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Loading required package: data.table
Attaching package: ‘data.table’
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between, first, last
Loading required package: igraph
Attaching package: ‘igraph’
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compose, simplify
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crossing
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as_data_frame
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as_data_frame, groups, union
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
Loading required package: dnet
Loading required package: supraHex
Loading required package: hexbin
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Warning messages:
1: replacing previous import 'ROCR::plot' by 'graphics::plot' when loading 'Pi'
2: no DISPLAY variable so Tk is not available
import numpy as np
import pandas as pd
res = pd.read_csv("/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/2021_03_18.PriorityIndex_MG.top30.drugability.tab",sep="\t")
res
| name | rank | rating | description | seed | nGene | cGene | eGene | dGene | pGene | fGene | Drugability | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AP1B1 | 25 | 3.867271 | adaptor related protein complex 1 beta 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1 | AP1G1 | 18 | 3.996396 | adaptor related protein complex 1 gamma 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | AP1M1 | 11 | 4.123101 | adaptor related protein complex 1 mu 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | AP1M2 | 8 | 4.150376 | adaptor related protein complex 1 mu 2 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | AP1S1 | 16 | 4.049329 | adaptor related protein complex 1 sigma 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | AP1S2 | 13 | 4.075882 | adaptor related protein complex 1 sigma 2 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | AP1S3 | 29 | 3.752658 | adaptor related protein complex 1 sigma 3 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7 | AP2A1 | 15 | 4.063403 | adaptor related protein complex 2 alpha 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | AP2A2 | 9 | 4.149004 | adaptor related protein complex 2 alpha 2 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | AP2B1 | 23 | 3.919930 | adaptor related protein complex 2 beta 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 10 | AP2M1 | 14 | 4.064006 | adaptor related protein complex 2 mu 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | AP2S1 | 10 | 4.142888 | adaptor related protein complex 2 sigma 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | AP4B1 | 3 | 4.525471 | adaptor related protein complex 4 beta 1 subunit | Y | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
| 13 | AP4E1 | 27 | 3.766819 | adaptor related protein complex 4 epsilon 1 su... | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | AP4S1 | 24 | 3.891437 | adaptor related protein complex 4 sigma 1 subunit | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | ARF1 | 22 | 3.925540 | ADP ribosylation factor 1 | N | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 16 | CD74 | 2 | 4.590645 | CD74 molecule | N | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 17 | CLTA | 26 | 3.786336 | clathrin light chain A | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18 | CLTCL1 | 30 | 3.752224 | clathrin heavy chain like 1 | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 19 | DCLRE1B | 7 | 4.330237 | DNA cross-link repair 1B | Y | 0 | 10 | 0 | 0 | 0 | 0 | 0 |
| 20 | HLA-C | 28 | 3.764621 | major histocompatibility complex, class I, C | Y | 0 | 0 | 2 | 1 | 0 | 1 | 1 |
| 21 | HLA-DQA1 | 5 | 4.436809 | major histocompatibility complex, class II, DQ... | Y | 1 | 5 | 0 | 1 | 1 | 1 | 8 |
| 22 | HLA-DQB1 | 17 | 4.047988 | major histocompatibility complex, class II, DQ... | N | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| 23 | HLA-DRA | 4 | 4.481652 | major histocompatibility complex, class II, DR... | Y | 0 | 6 | 0 | 1 | 0 | 1 | 21 |
| 24 | HLA-DRB1 | 1 | 5.000000 | major histocompatibility complex, class II, DR... | Y | 1 | 0 | 0 | 1 | 0 | 0 | 18 |
| 25 | HSPA8 | 12 | 4.109843 | heat shock protein family A (Hsp70) member 8 | N | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| 26 | PINX1 | 21 | 3.949816 | PIN2/TERF1 interacting, telomerase inhibitor 1 | N | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 27 | PSMD4 | 20 | 3.957381 | proteasome 26S subunit, non-ATPase 4 | N | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
| 28 | SH3GL2 | 19 | 3.960750 | SH3 domain containing GRB2 like 2, endophilin A1 | N | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| 29 | UBA52 | 6 | 4.424590 | ubiquitin A-52 residue ribosomal protein fusio... | N | 0 | 0 | 0 | 0 | 0 | 0 | 19 |
from IPython.display import Image
Image(filename="/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/gp_manhattan.png")
from IPython.display import Image
Image(filename="/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/PI.reactome.png")
import numpy as np
import pandas as pd
res = pd.read_csv("PriorityIndex/2021_03_20.DrugsOP.PI.csv",sep=",")
res
| TargetGene | Disease | Disease ID | Drug | Drug ID | Type | Mechanism of action | Action type | Activity | Target class | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CD74 | chronic lymphocytic leukemia | EFO_0000095 | MILATUZUMAB | CHEMBL1743040 | Antibody | HLA-DR antigens-associated invariant chain ant... | Antagonist | Negative modulator | Surface antigen |
| 1 | CD74 | lymphoma | EFO_0000574 | MILATUZUMAB | CHEMBL1743040 | Antibody | HLA-DR antigens-associated invariant chain ant... | Antagonist | Negative modulator | Surface antigen |
| 2 | CD74 | multiple myeloma | EFO_0001378 | MILATUZUMAB | CHEMBL1743040 | Antibody | HLA-DR antigens-associated invariant chain ant... | Antagonist | Negative modulator | Surface antigen |
| 3 | CD74 | plasmacytoma | EFO_0006738 | MILATUZUMAB | CHEMBL1743040 | Antibody | HLA-DR antigens-associated invariant chain ant... | Antagonist | Negative modulator | Surface antigen |
| 4 | HLA-DRB1 | chronic lymphocytic leukemia | EFO_0000095 | APOLIZUMAB | CHEMBL2108252 | Antibody | Human leukocyte antigen DR beta chain inhibitor | Inhibitor | Negative modulator | Surface antigen |
| 5 | HLA-DRB1 | diffuse large B-cell lymphoma | EFO_0000403 | APOLIZUMAB | CHEMBL2108252 | Antibody | Human leukocyte antigen DR beta chain inhibitor | Inhibitor | Negative modulator | Surface antigen |
| 6 | HLA-DRB1 | follicular lymphoma | MONDO_0018906 | APOLIZUMAB | CHEMBL2108252 | Antibody | Human leukocyte antigen DR beta chain inhibitor | Inhibitor | Negative modulator | Surface antigen |
| 7 | HLA-DRB1 | lymphoma | EFO_0000574 | APOLIZUMAB | CHEMBL2108252 | Antibody | Human leukocyte antigen DR beta chain inhibitor | Inhibitor | Negative modulator | Surface antigen |
| 8 | HLA-DRB1 | Mantle cell lymphoma | EFO_1001469 | APOLIZUMAB | CHEMBL2108252 | Antibody | Human leukocyte antigen DR beta chain inhibitor | Inhibitor | Negative modulator | Surface antigen |
| 9 | HLA-DRB1 | relapsing-remitting multiple sclerosis | EFO_0003929 | PLOVAMER ACETATE | CHEMBL2108712 | Small molecule | HLA class II histocompatibility antigen, DRB1-... | Modulator | Other | Surface antigen |
| 10 | HSPA8 | systemic lupus erythematosus | EFO_0002690 | FORIGERIMOD | CHEMBL3989409 | Protein | Heat shock cognate 71 kDa protein inhibitor | Inhibitor | Negative modulator | Other cytosolic protein |
| 11 | PSMD4 | cancer | EFO_0000311 | OPROZOMIB | CHEMBL2103884 | Small molecule | 26S proteosome inhibitor | Inhibitor | Negative modulator | Enzyme |
| 12 | PSMD4 | hepatocellular carcinoma | EFO_0000182 | OPROZOMIB | CHEMBL2103884 | Small molecule | 26S proteosome inhibitor | Inhibitor | Negative modulator | Enzyme |
| 13 | PSMD4 | multiple myeloma | EFO_0001378 | OPROZOMIB | CHEMBL2103884 | Small molecule | 26S proteosome inhibitor | Inhibitor | Negative modulator | Enzyme |
| 14 | PSMD4 | neoplasm | EFO_0000616 | OPROZOMIB | CHEMBL2103884 | Small molecule | 26S proteosome inhibitor | Inhibitor | Negative modulator | Enzyme |
| 15 | PSMD4 | Waldenstrom macroglobulinemia | EFO_0009441 | OPROZOMIB | CHEMBL2103884 | Small molecule | 26S proteosome inhibitor | Inhibitor | Negative modulator | Enzyme |
%%bash
cd /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex
echo "name rank
HLA-DRB1 1
AP4B1 2
CD74 3
HLA-DRA 4
HLA-DQA1 5
AP1M2 6
DCLRE1B 7
UBA52 8
AP1M1 9
AP2A2 10
AP2S1 11
AP1S2 12
AP1S1 13
AP4S1 14
AP2M1 15
AP2A1 16
AP1G1 17
ARF1 18
HSPA8 19
AP4E1 20
CLHC1 21
CLTCL1 22
SH3GL2 23
AP5M1 24
AP1G2 25
AP2B1 26
AP1B1 27
AP4M1 28
AP3D1 29
PSMD4 30" > PriorityIndex.OLDversion.txt
%%bash
cd /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex
module load R/4.0.3
R --vanilla --no-save
require("readr")
require(dplyr)
require(tidyverse)
require(data.table)
library(Pi)
current = fread ("Drugability.curated.top30.txt", header = T)
data1 = as.character(current$name)
dim(current)
head(data1)
previous = fread("PriorityIndex.OLDversion.txt", header = T)
data2 = as.character(previous$name)
dim(previous)
antijoinCurrent = anti_join(current, previous, by="name")
antijoinCurrent
antijoinPrevious = anti_join(previous, current, by="name")
antijoinPrevious
library(VennDiagram)
venn.diagram(x = list(data1, data2),
category.names = c("Current Analysis" , "Previous"),
filename = 'PI_vennDiagramm.png',
output=TRUE)
R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
>
> require("readr")
> require(dplyr)
> require(tidyverse)
> require(data.table)
> library(Pi)
> current = fread ("Drugability.curated.top30.txt", header = T)
> data1 = as.character(current$name)
> dim(current)
[1] 30 2
> head(data1)
[1] "HLA-DRB1" "CD74" "AP4B1" "HLA-DRA" "HLA-DQA1" "UBA52"
> previous = fread("PriorityIndex.OLDversion.txt", header = T)
> data2 = as.character(previous$name)
> dim(previous)
[1] 30 2
>
> antijoinCurrent = anti_join(current, previous, by="name")
> antijoinCurrent
name Drugability
1: HLA-DQB1 8
2: PINX1 0
3: CLTA 0
4: HLA-C 1
5: AP1S3 1
>
> antijoinPrevious = anti_join(previous, current, by="name")
> antijoinPrevious
name rank
1: CLHC1 21
2: AP5M1 24
3: AP1G2 25
4: AP4M1 28
5: AP3D1 29
>
>
> library(VennDiagram)
> venn.diagram(x = list(data1, data2),
+ category.names = c("Current Analysis" , "Previous"),
+ filename = 'PI_vennDiagramm.png',
+ output=TRUE)
[1] 1
>
>
[-] Unloading gcc 9.2.0 ...
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[+] Loading gcc 9.2.0 ...
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[+] Loading gcc 9.2.0 ...
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[+] Loading R 4.0.3
The following have been reloaded with a version change:
1) R/4.0 => R/4.0.3
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Loading required package: dplyr
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filter, lag
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Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
✔ ggplot2 3.3.3 ✔ purrr 0.3.4
✔ tibble 3.1.0 ✔ stringr 1.4.0
✔ tidyr 1.1.3 ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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Loading required package: data.table
Attaching package: ‘data.table’
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between, first, last
Loading required package: igraph
Attaching package: ‘igraph’
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compose, simplify
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crossing
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as_data_frame
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as_data_frame, groups, union
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decompose, spectrum
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union
Loading required package: dnet
Loading required package: supraHex
Loading required package: hexbin
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Warning messages:
1: replacing previous import 'ROCR::plot' by 'graphics::plot' when loading 'Pi'
2: no DISPLAY variable so Tk is not available
Loading required package: grid
Loading required package: futile.logger
from IPython.display import Image
Image(filename="/data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/PI_vennDiagramm.png")
%%bash
#mkdir Ruth_MGpaper
cd Ruth_MGpaper
mkdir PriorityIndex
scp /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/2021_03_18.PriorityIndex_MG.top30.drugability.tab /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/Ruth_MGpaper/PriorityIndex
scp /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/2021_03_20.DrugsOP.PI.csv /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/Ruth_MGpaper/PriorityIndex
scp /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/2021_03_18.PriorityIndex_MG.tab /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/Ruth_MGpaper/PriorityIndex
scp /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/PriorityIndex/OpenTarget_output.csv /data/ALS_50k/SaraSaez_ALS/2021-03-18.COLABORATION_MG/Ruth_MGpaper/PriorityIndex